"The ultimate test of a machine learning algorithm is its ability to perform well on new, unseen data."
机器学习算法的最终测试是它在新的、未见过的数据上表现良好的能力。
"The future of machine learning will depend on our ability to develop algorithms that can learn from complex, high-dimensional data."
机器学习的未来将取决于我们开发能够从复杂、高维数据中学习的算法的能力。
"One of the most important challenges in machine learning is to develop algorithms that can learn from small amounts of data."
机器学习中最重要的挑战之一是开发能够从少量数据中学习的算法。
"The power of machine learning lies in its ability to discover patterns and relationships in data that are not immediately obvious to human observers."
机器学习的力量在于它能够发现数据中人类观察者不易立即察觉的模式和关系。
"In machine learning, the goal is not just to fit the data, but to find a model that captures the underlying structure of the data."
在机器学习中，目标不仅仅是拟合数据，而是找到一个能够捕捉数据潜在结构的模型。
"The success of machine learning depends on the ability to balance the trade-off between bias and variance in the models we build."
机器学习的成功取决于我们构建的模型中偏差和方差之间权衡的能力。
"A key insight in computational learning theory is that the complexity of a learning problem is determined by the complexity of the hypothesis space and the amount of data available."
计算学习理论中的一个关键见解是，学习问题的复杂性由假设空间的复杂性和可用数据的数量决定。
"The challenge in machine learning is not just to find patterns in data, but to find patterns that generalize to new, unseen data."
机器学习中的挑战不仅仅是发现数据中的模式，而是发现能够推广到新的、未见过的数据的模式。
"Computational learning theory provides a mathematical framework for understanding the capabilities and limitations of machine learning algorithms."
计算学习理论为理解机器学习算法的能力和局限性提供了一个数学框架。
机器学习的最终目标是制造能够从经验中学习并随时间提高性能的机器。
The future of machine learning lies in the development of algorithms that can learn from fewer examples and generalize more effectively.
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